Bigtable is one of the prototypical examples of a wide column store. It maps two arbitrary string values (row key and column key) and timestamp (hence three-dimensional mapping) into an associated arbitrary byte array. It is not a relational database and can be better defined as a sparse, distributed multi-dimensional sorted map.[4]:1 Bigtable is designed to scale into the petabyte range across "hundreds or thousands of machines, and to make it easy to add more machines [to] the system and automatically start taking advantage of those resources without any reconfiguration".[11]

Each table has multiple dimensions (one of which is a field for time, allowing for versioning and garbage collection). Tables are optimized for Google File System (GFS) by being split into multiple tablets – segments of the table are split along a row chosen such that the tablet will be ~200 megabytes in size. When sizes threaten to grow beyond a specified limit, the tablets are compressed using the algorithm BMDiff[12][13] and the Zippy compression algorithm[14] publicly known and open-sourced as Snappy,[15] which is a less space-optimal variation of LZ77 but more efficient in terms of computing time. The locations in the GFS of tablets are recorded as database entries in multiple special tablets, which are called "META1" tablets. META1 tablets are found by querying the single "META0" tablet, which typically resides on a server of its own since it is often queried by clients as to the location of the "META1" tablet which itself has the answer to the question of where the actual data is located. Like GFS's master server, the META0 server is not generally a bottleneck since the processor time and bandwidth necessary to discover and transmit META1 locations is minimal and clients aggressively cache locations to minimize queries.

^Kumar, Aswini, Whitchcock, Andrew, ed., Google's Bigtable, First an overview. Bigtable has been in development since early 2004 and has been in active use for about eight months (about February 2005)..

^Chang et al. 2006, p. 3: ‘Bigtable can be used with MapReduce, a framework for running large-scale parallel computations developed at Google. We have written a set of wrappers that allow a Bigtable to be used both as an input source and as an output target for MapReduce jobs’

^Whitchcock, Andrew, Google's Bigtable, There are currently around 100 cells for services such as Print, Search History, Maps, and Orkut.

^Cordes, Kyle (2007-07-12), YouTube Scalability(talk), Their new solution for thumbnails is to use Google’s Bigtable, which provides high performance for a large number of rows, fault tolerance, caching, etc. This is a nice (and rare?) example of actual synergy in an acquisition..

^Chang et al. 2006, Conclusion: ‘We have described Bigtable, a distributed system for storing structured data at Google... Our users like the performance and high availability provided by the Bigtable implementation, and that they can scale the capacity of their clusters by simply adding more machines to the system as their resource demands change over time... Finally, we have found that there are significant advantages to building our own storage solution at Google. We have gotten a substantial amount of flexibility from designing our own data model for Bigtable.’